Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations251079
Missing cells27459
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.7 MiB
Average record size in memory120.0 B

Variable types

Numeric2
Categorical3
Text10

Alerts

Unnamed: 0 is highly overall correlated with brandHigh correlation
brand is highly overall correlated with Unnamed: 0High correlation
fuel_consumption_l_100km has 26873 (10.7%) missing values Missing
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unique

Reproduction

Analysis started2025-03-29 13:48:06.851414
Analysis finished2025-03-29 13:48:38.790915
Duration31.94 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct251079
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125539
Minimum0
Maximum251078
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-29T14:48:39.235200image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12553.9
Q162769.5
median125539
Q3188308.5
95-th percentile238524.1
Maximum251078
Range251078
Interquartile range (IQR)125539

Descriptive statistics

Standard deviation72480.408
Coefficient of variation (CV)0.57735372
Kurtosis-1.2
Mean125539
Median Absolute Deviation (MAD)62770
Skewness0
Sum3.1520207 × 1010
Variance5.2534096 × 109
MonotonicityStrictly increasing
2025-03-29T14:48:39.567420image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
251078 1
 
< 0.1%
0 1
 
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
251062 1
 
< 0.1%
251061 1
 
< 0.1%
251060 1
 
< 0.1%
Other values (251069) 251069
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
251078 1
< 0.1%
251077 1
< 0.1%
251076 1
< 0.1%
251075 1
< 0.1%
251074 1
< 0.1%
251073 1
< 0.1%
251072 1
< 0.1%
251071 1
< 0.1%
251070 1
< 0.1%
251069 1
< 0.1%

brand
Categorical

High correlation 

Distinct47
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
volkswagen
33281 
mercedes-benz
27226 
audi
21161 
opel
20388 
bmw
19810 
Other values (42)
129213 

Length

Max length13
Median length11
Mean length6.439304
Min length3

Characters and Unicode

Total characters1616774
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowalfa-romeo
2nd rowalfa-romeo
3rd rowalfa-romeo
4th rowalfa-romeo
5th rowalfa-romeo

Common Values

ValueCountFrequency (%)
volkswagen 33281
13.3%
mercedes-benz 27226
10.8%
audi 21161
 
8.4%
opel 20388
 
8.1%
bmw 19810
 
7.9%
ford 18790
 
7.5%
skoda 14039
 
5.6%
seat 11949
 
4.8%
renault 8694
 
3.5%
toyota 8228
 
3.3%
Other values (37) 67513
26.9%

Length

2025-03-29T14:48:39.802689image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
volkswagen 33281
13.3%
mercedes-benz 27226
10.8%
audi 21161
 
8.4%
opel 20388
 
8.1%
bmw 19810
 
7.9%
ford 18790
 
7.5%
skoda 14039
 
5.6%
seat 11949
 
4.8%
renault 8694
 
3.5%
toyota 8228
 
3.3%
Other values (37) 67513
26.9%

Most occurring characters

ValueCountFrequency (%)
e 214526
 
13.3%
a 150309
 
9.3%
o 133918
 
8.3%
s 103866
 
6.4%
d 101592
 
6.3%
n 98464
 
6.1%
r 76839
 
4.8%
l 72772
 
4.5%
i 64956
 
4.0%
m 60892
 
3.8%
Other values (15) 538640
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1616774
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 214526
 
13.3%
a 150309
 
9.3%
o 133918
 
8.3%
s 103866
 
6.4%
d 101592
 
6.3%
n 98464
 
6.1%
r 76839
 
4.8%
l 72772
 
4.5%
i 64956
 
4.0%
m 60892
 
3.8%
Other values (15) 538640
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1616774
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 214526
 
13.3%
a 150309
 
9.3%
o 133918
 
8.3%
s 103866
 
6.4%
d 101592
 
6.3%
n 98464
 
6.1%
r 76839
 
4.8%
l 72772
 
4.5%
i 64956
 
4.0%
m 60892
 
3.8%
Other values (15) 538640
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1616774
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 214526
 
13.3%
a 150309
 
9.3%
o 133918
 
8.3%
s 103866
 
6.4%
d 101592
 
6.3%
n 98464
 
6.1%
r 76839
 
4.8%
l 72772
 
4.5%
i 64956
 
4.0%
m 60892
 
3.8%
Other values (15) 538640
33.3%

model
Text

Distinct1312
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
2025-03-29T14:48:40.858558image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length33
Median length28
Mean length12.960865
Min length3

Characters and Unicode

Total characters3254201
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique121 ?
Unique (%)< 0.1%

Sample

1st rowAlfa Romeo GTV
2nd rowAlfa Romeo 164
3rd rowAlfa Romeo Spider
4th rowAlfa Romeo Spider
5th rowAlfa Romeo 164
ValueCountFrequency (%)
volkswagen 33281
 
5.9%
mercedes-benz 27226
 
4.8%
audi 21161
 
3.7%
opel 20388
 
3.6%
bmw 19810
 
3.5%
ford 18790
 
3.3%
skoda 14039
 
2.5%
seat 11949
 
2.1%
golf 10600
 
1.9%
renault 8694
 
1.5%
Other values (984) 378678
67.1%
2025-03-29T14:48:42.246669image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
313537
 
9.6%
e 266619
 
8.2%
a 243348
 
7.5%
o 211184
 
6.5%
n 146559
 
4.5%
r 140155
 
4.3%
s 126681
 
3.9%
d 118457
 
3.6%
i 115766
 
3.6%
l 103441
 
3.2%
Other values (63) 1468454
45.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3254201
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
313537
 
9.6%
e 266619
 
8.2%
a 243348
 
7.5%
o 211184
 
6.5%
n 146559
 
4.5%
r 140155
 
4.3%
s 126681
 
3.9%
d 118457
 
3.6%
i 115766
 
3.6%
l 103441
 
3.2%
Other values (63) 1468454
45.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3254201
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
313537
 
9.6%
e 266619
 
8.2%
a 243348
 
7.5%
o 211184
 
6.5%
n 146559
 
4.5%
r 140155
 
4.3%
s 126681
 
3.9%
d 118457
 
3.6%
i 115766
 
3.6%
l 103441
 
3.2%
Other values (63) 1468454
45.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3254201
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
313537
 
9.6%
e 266619
 
8.2%
a 243348
 
7.5%
o 211184
 
6.5%
n 146559
 
4.5%
r 140155
 
4.3%
s 126681
 
3.9%
d 118457
 
3.6%
i 115766
 
3.6%
l 103441
 
3.2%
Other values (63) 1468454
45.1%

color
Categorical

Distinct14
Distinct (%)< 0.1%
Missing166
Missing (%)0.1%
Memory size1.9 MiB
black
58720 
grey
46786 
white
40640 
silver
34362 
blue
32092 
Other values (9)
38313 

Length

Max length6
Median length5
Mean length4.6752978
Min length3

Characters and Unicode

Total characters1173093
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowred
2nd rowblack
3rd rowblack
4th rowblack
5th rowred

Common Values

ValueCountFrequency (%)
black 58720
23.4%
grey 46786
18.6%
white 40640
16.2%
silver 34362
13.7%
blue 32092
12.8%
red 21258
 
8.5%
brown 4415
 
1.8%
green 3500
 
1.4%
orange 3367
 
1.3%
beige 2420
 
1.0%
Other values (4) 3353
 
1.3%

Length

2025-03-29T14:48:42.484483image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
black 58720
23.4%
grey 46786
18.6%
white 40640
16.2%
silver 34362
13.7%
blue 32092
12.8%
red 21258
 
8.5%
brown 4415
 
1.8%
green 3500
 
1.4%
orange 3367
 
1.3%
beige 2420
 
1.0%
Other values (4) 3353
 
1.3%

Most occurring characters

ValueCountFrequency (%)
e 193118
16.5%
l 129720
11.1%
r 114274
9.7%
b 98233
 
8.4%
i 77830
 
6.6%
a 62087
 
5.3%
c 58720
 
5.0%
k 58720
 
5.0%
g 56653
 
4.8%
y 48565
 
4.1%
Other values (10) 275173
23.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1173093
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 193118
16.5%
l 129720
11.1%
r 114274
9.7%
b 98233
 
8.4%
i 77830
 
6.6%
a 62087
 
5.3%
c 58720
 
5.0%
k 58720
 
5.0%
g 56653
 
4.8%
y 48565
 
4.1%
Other values (10) 275173
23.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1173093
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 193118
16.5%
l 129720
11.1%
r 114274
9.7%
b 98233
 
8.4%
i 77830
 
6.6%
a 62087
 
5.3%
c 58720
 
5.0%
k 58720
 
5.0%
g 56653
 
4.8%
y 48565
 
4.1%
Other values (10) 275173
23.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1173093
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 193118
16.5%
l 129720
11.1%
r 114274
9.7%
b 98233
 
8.4%
i 77830
 
6.6%
a 62087
 
5.3%
c 58720
 
5.0%
k 58720
 
5.0%
g 56653
 
4.8%
y 48565
 
4.1%
Other values (10) 275173
23.5%
Distinct433
Distinct (%)0.2%
Missing4
Missing (%)< 0.1%
Memory size1.9 MiB
2025-03-29T14:48:43.155078image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length13
Median length7
Mean length7.0001155
Min length2

Characters and Unicode

Total characters1757554
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83 ?
Unique (%)< 0.1%

Sample

1st row10/1995
2nd row02/1995
3rd row02/1995
4th row07/1995
5th row11/1996
ValueCountFrequency (%)
03/2023 4746
 
1.9%
05/2023 4128
 
1.6%
02/2023 3430
 
1.4%
04/2023 3250
 
1.3%
01/2023 2996
 
1.2%
05/2019 2920
 
1.2%
03/2019 2904
 
1.2%
04/2019 2729
 
1.1%
06/2023 2543
 
1.0%
06/2019 2532
 
1.0%
Other values (426) 218943
87.2%
2025-03-29T14:48:43.814927image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 517673
29.5%
2 394771
22.5%
1 264667
15.1%
/ 250882
14.3%
3 59119
 
3.4%
9 58182
 
3.3%
8 46460
 
2.6%
6 43628
 
2.5%
7 43127
 
2.5%
5 41426
 
2.4%
Other values (39) 37619
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1757554
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 517673
29.5%
2 394771
22.5%
1 264667
15.1%
/ 250882
14.3%
3 59119
 
3.4%
9 58182
 
3.3%
8 46460
 
2.6%
6 43628
 
2.5%
7 43127
 
2.5%
5 41426
 
2.4%
Other values (39) 37619
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1757554
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 517673
29.5%
2 394771
22.5%
1 264667
15.1%
/ 250882
14.3%
3 59119
 
3.4%
9 58182
 
3.3%
8 46460
 
2.6%
6 43628
 
2.5%
7 43127
 
2.5%
5 41426
 
2.4%
Other values (39) 37619
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1757554
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 517673
29.5%
2 394771
22.5%
1 264667
15.1%
/ 250882
14.3%
3 59119
 
3.4%
9 58182
 
3.3%
8 46460
 
2.6%
6 43628
 
2.5%
7 43127
 
2.5%
5 41426
 
2.4%
Other values (39) 37619
 
2.1%

year
Text

Distinct91
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
2025-03-29T14:48:43.955967image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length10
Median length4
Mean length4.0021308
Min length3

Characters and Unicode

Total characters1004851
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)< 0.1%

Sample

1st row1995
2nd row1995
3rd row1995
4th row1995
5th row1996
ValueCountFrequency (%)
2019 29225
11.6%
2018 24095
 
9.6%
2023 21097
 
8.4%
2022 20653
 
8.2%
2017 18940
 
7.5%
2020 18566
 
7.4%
2021 16022
 
6.4%
2016 15072
 
6.0%
2015 12712
 
5.1%
2014 10623
 
4.2%
Other values (82) 64093
25.5%
2025-03-29T14:48:44.340418image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 355576
35.4%
0 305808
30.4%
1 168039
16.7%
9 40320
 
4.0%
3 32554
 
3.2%
8 29422
 
2.9%
7 23313
 
2.3%
6 19284
 
1.9%
5 16299
 
1.6%
4 13286
 
1.3%
Other values (29) 950
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1004851
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 355576
35.4%
0 305808
30.4%
1 168039
16.7%
9 40320
 
4.0%
3 32554
 
3.2%
8 29422
 
2.9%
7 23313
 
2.3%
6 19284
 
1.9%
5 16299
 
1.6%
4 13286
 
1.3%
Other values (29) 950
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1004851
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 355576
35.4%
0 305808
30.4%
1 168039
16.7%
9 40320
 
4.0%
3 32554
 
3.2%
8 29422
 
2.9%
7 23313
 
2.3%
6 19284
 
1.9%
5 16299
 
1.6%
4 13286
 
1.3%
Other values (29) 950
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1004851
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 355576
35.4%
0 305808
30.4%
1 168039
16.7%
9 40320
 
4.0%
3 32554
 
3.2%
8 29422
 
2.9%
7 23313
 
2.3%
6 19284
 
1.9%
5 16299
 
1.6%
4 13286
 
1.3%
Other values (29) 950
 
0.1%
Distinct18228
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
2025-03-29T14:48:44.992581image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length32
Median length5
Mean length4.818129
Min length2

Characters and Unicode

Total characters1209731
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8539 ?
Unique (%)3.4%

Sample

1st row1300
2nd row24900
3rd row5900
4th row4900
5th row17950
ValueCountFrequency (%)
19990 1592
 
0.6%
16990 1404
 
0.6%
17990 1373
 
0.5%
15990 1316
 
0.5%
14990 1303
 
0.5%
18990 1281
 
0.5%
13990 1191
 
0.5%
24990 1157
 
0.5%
12990 1111
 
0.4%
22990 1074
 
0.4%
Other values (18238) 238424
94.9%
2025-03-29T14:48:45.614681image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 299465
24.8%
9 261963
21.7%
1 115823
 
9.6%
2 101666
 
8.4%
5 91997
 
7.6%
8 86034
 
7.1%
4 83414
 
6.9%
3 69382
 
5.7%
7 52981
 
4.4%
6 45065
 
3.7%
Other values (60) 1941
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1209731
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 299465
24.8%
9 261963
21.7%
1 115823
 
9.6%
2 101666
 
8.4%
5 91997
 
7.6%
8 86034
 
7.1%
4 83414
 
6.9%
3 69382
 
5.7%
7 52981
 
4.4%
6 45065
 
3.7%
Other values (60) 1941
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1209731
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 299465
24.8%
9 261963
21.7%
1 115823
 
9.6%
2 101666
 
8.4%
5 91997
 
7.6%
8 86034
 
7.1%
4 83414
 
6.9%
3 69382
 
5.7%
7 52981
 
4.4%
6 45065
 
3.7%
Other values (60) 1941
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1209731
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 299465
24.8%
9 261963
21.7%
1 115823
 
9.6%
2 101666
 
8.4%
5 91997
 
7.6%
8 86034
 
7.1%
4 83414
 
6.9%
3 69382
 
5.7%
7 52981
 
4.4%
6 45065
 
3.7%
Other values (60) 1941
 
0.2%
Distinct596
Distinct (%)0.2%
Missing134
Missing (%)0.1%
Memory size1.9 MiB
2025-03-29T14:48:46.242266image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length17
Median length3
Mean length2.6023272
Min length1

Characters and Unicode

Total characters653041
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique118 ?
Unique (%)< 0.1%

Sample

1st row148
2nd row191
3rd row110
4th row110
5th row132
ValueCountFrequency (%)
110 25986
 
10.3%
140 9865
 
3.9%
96 9507
 
3.8%
81 9055
 
3.6%
85 8987
 
3.6%
103 8297
 
3.3%
100 6553
 
2.6%
74 6202
 
2.5%
135 5999
 
2.4%
92 5783
 
2.3%
Other values (559) 154940
61.7%
2025-03-29T14:48:47.170153image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 177730
27.2%
0 109346
16.7%
5 69903
 
10.7%
2 50427
 
7.7%
8 44977
 
6.9%
3 43831
 
6.7%
4 42685
 
6.5%
7 40850
 
6.3%
6 38139
 
5.8%
9 34158
 
5.2%
Other values (27) 995
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 653041
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 177730
27.2%
0 109346
16.7%
5 69903
 
10.7%
2 50427
 
7.7%
8 44977
 
6.9%
3 43831
 
6.7%
4 42685
 
6.5%
7 40850
 
6.3%
6 38139
 
5.8%
9 34158
 
5.2%
Other values (27) 995
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 653041
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 177730
27.2%
0 109346
16.7%
5 69903
 
10.7%
2 50427
 
7.7%
8 44977
 
6.9%
3 43831
 
6.7%
4 42685
 
6.5%
7 40850
 
6.3%
6 38139
 
5.8%
9 34158
 
5.2%
Other values (27) 995
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 653041
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 177730
27.2%
0 109346
16.7%
5 69903
 
10.7%
2 50427
 
7.7%
8 44977
 
6.9%
3 43831
 
6.7%
4 42685
 
6.5%
7 40850
 
6.3%
6 38139
 
5.8%
9 34158
 
5.2%
Other values (27) 995
 
0.2%
Distinct578
Distinct (%)0.2%
Missing129
Missing (%)0.1%
Memory size1.9 MiB
2025-03-29T14:48:47.868674image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length17
Median length3
Mean length2.8534728
Min length1

Characters and Unicode

Total characters716079
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique117 ?
Unique (%)< 0.1%

Sample

1st row201
2nd row260
3rd row150
4th row150
5th row179
ValueCountFrequency (%)
150 25987
 
10.4%
190 9866
 
3.9%
131 9507
 
3.8%
110 9068
 
3.6%
116 8987
 
3.6%
140 8298
 
3.3%
136 6554
 
2.6%
101 6202
 
2.5%
184 5999
 
2.4%
125 5785
 
2.3%
Other values (551) 154820
61.7%
2025-03-29T14:48:48.843744image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 208758
29.2%
0 121450
17.0%
2 73507
 
10.3%
5 71949
 
10.0%
3 45526
 
6.4%
6 45060
 
6.3%
9 44281
 
6.2%
4 43522
 
6.1%
7 33309
 
4.7%
8 28194
 
3.9%
Other values (26) 523
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 716079
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 208758
29.2%
0 121450
17.0%
2 73507
 
10.3%
5 71949
 
10.0%
3 45526
 
6.4%
6 45060
 
6.3%
9 44281
 
6.2%
4 43522
 
6.1%
7 33309
 
4.7%
8 28194
 
3.9%
Other values (26) 523
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 716079
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 208758
29.2%
0 121450
17.0%
2 73507
 
10.3%
5 71949
 
10.0%
3 45526
 
6.4%
6 45060
 
6.3%
9 44281
 
6.2%
4 43522
 
6.1%
7 33309
 
4.7%
8 28194
 
3.9%
Other values (26) 523
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 716079
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 208758
29.2%
0 121450
17.0%
2 73507
 
10.3%
5 71949
 
10.0%
3 45526
 
6.4%
6 45060
 
6.3%
9 44281
 
6.2%
4 43522
 
6.1%
7 33309
 
4.7%
8 28194
 
3.9%
Other values (26) 523
 
0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
Automatic
131749 
Manual
117869 
Unknown
 
1144
Semi-automatic
 
317

Length

Max length14
Median length9
Mean length7.5888505
Min length6

Characters and Unicode

Total characters1905401
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManual
2nd rowManual
3rd rowUnknown
4th rowManual
5th rowManual

Common Values

ValueCountFrequency (%)
Automatic 131749
52.5%
Manual 117869
46.9%
Unknown 1144
 
0.5%
Semi-automatic 317
 
0.1%

Length

2025-03-29T14:48:49.112844image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-29T14:48:49.355472image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
automatic 131749
52.5%
manual 117869
46.9%
unknown 1144
 
0.5%
semi-automatic 317
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 368121
19.3%
t 264132
13.9%
u 249935
13.1%
o 133210
 
7.0%
m 132383
 
6.9%
i 132383
 
6.9%
c 132066
 
6.9%
A 131749
 
6.9%
n 121301
 
6.4%
M 117869
 
6.2%
Other values (7) 122252
 
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1905401
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 368121
19.3%
t 264132
13.9%
u 249935
13.1%
o 133210
 
7.0%
m 132383
 
6.9%
i 132383
 
6.9%
c 132066
 
6.9%
A 131749
 
6.9%
n 121301
 
6.4%
M 117869
 
6.2%
Other values (7) 122252
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1905401
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 368121
19.3%
t 264132
13.9%
u 249935
13.1%
o 133210
 
7.0%
m 132383
 
6.9%
i 132383
 
6.9%
c 132066
 
6.9%
A 131749
 
6.9%
n 121301
 
6.4%
M 117869
 
6.2%
Other values (7) 122252
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1905401
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 368121
19.3%
t 264132
13.9%
u 249935
13.1%
o 133210
 
7.0%
m 132383
 
6.9%
i 132383
 
6.9%
c 132066
 
6.9%
A 131749
 
6.9%
n 121301
 
6.4%
M 117869
 
6.2%
Other values (7) 122252
 
6.4%
Distinct136
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
2025-03-29T14:48:49.643713image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length13
Median length6
Mean length6.0411544
Min length3

Characters and Unicode

Total characters1516807
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique109 ?
Unique (%)< 0.1%

Sample

1st rowPetrol
2nd rowPetrol
3rd rowPetrol
4th rowPetrol
5th rowPetrol
ValueCountFrequency (%)
petrol 143280
56.9%
diesel 86897
34.5%
hybrid 13083
 
5.2%
electric 5967
 
2.4%
lpg 1255
 
0.5%
cng 508
 
0.2%
other 178
 
0.1%
unknown 96
 
< 0.1%
hydrogen 82
 
< 0.1%
km 34
 
< 0.1%
Other values (128) 211
 
0.1%
2025-03-29T14:48:50.103523image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 323301
21.3%
l 236172
15.6%
r 162590
10.7%
t 149487
9.9%
P 144536
9.5%
o 143494
9.5%
i 105973
 
7.0%
D 86899
 
5.7%
s 86897
 
5.7%
H 13165
 
0.9%
Other values (39) 64293
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1516807
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 323301
21.3%
l 236172
15.6%
r 162590
10.7%
t 149487
9.9%
P 144536
9.5%
o 143494
9.5%
i 105973
 
7.0%
D 86899
 
5.7%
s 86897
 
5.7%
H 13165
 
0.9%
Other values (39) 64293
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1516807
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 323301
21.3%
l 236172
15.6%
r 162590
10.7%
t 149487
9.9%
P 144536
9.5%
o 143494
9.5%
i 105973
 
7.0%
D 86899
 
5.7%
s 86897
 
5.7%
H 13165
 
0.9%
Other values (39) 64293
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1516807
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 323301
21.3%
l 236172
15.6%
r 162590
10.7%
t 149487
9.9%
P 144536
9.5%
o 143494
9.5%
i 105973
 
7.0%
D 86899
 
5.7%
s 86897
 
5.7%
H 13165
 
0.9%
Other values (39) 64293
 
4.2%
Distinct621
Distinct (%)0.3%
Missing26873
Missing (%)10.7%
Memory size1.9 MiB
2025-03-29T14:48:50.763088image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length17
Median length12
Mean length11.843791
Min length4

Characters and Unicode

Total characters2655449
Distinct characters45
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique238 ?
Unique (%)0.1%

Sample

1st row10,9 l/100 km
2nd row9,5 l/100 km
3rd row7,2 l/100 km
4th row9,5 l/100 km
5th row8,8 l/100 km
ValueCountFrequency (%)
km 224008
33.3%
l/100 222955
33.2%
4,9 8168
 
1.2%
5,1 7658
 
1.1%
5,5 7620
 
1.1%
5,9 7521
 
1.1%
5,3 7458
 
1.1%
5 6996
 
1.0%
5,7 6449
 
1.0%
5,4 6417
 
1.0%
Other values (499) 167069
24.8%
2025-03-29T14:48:51.662944image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 450193
17.0%
448113
16.9%
1 260509
9.8%
k 224668
8.5%
m 224107
8.4%
/ 223634
8.4%
l 222988
8.4%
, 200655
7.6%
5 93226
 
3.5%
4 70962
 
2.7%
Other values (35) 236394
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2655449
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 450193
17.0%
448113
16.9%
1 260509
9.8%
k 224668
8.5%
m 224107
8.4%
/ 223634
8.4%
l 222988
8.4%
, 200655
7.6%
5 93226
 
3.5%
4 70962
 
2.7%
Other values (35) 236394
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2655449
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 450193
17.0%
448113
16.9%
1 260509
9.8%
k 224668
8.5%
m 224107
8.4%
/ 223634
8.4%
l 222988
8.4%
, 200655
7.6%
5 93226
 
3.5%
4 70962
 
2.7%
Other values (35) 236394
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2655449
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 450193
17.0%
448113
16.9%
1 260509
9.8%
k 224668
8.5%
m 224107
8.4%
/ 223634
8.4%
l 222988
8.4%
, 200655
7.6%
5 93226
 
3.5%
4 70962
 
2.7%
Other values (35) 236394
8.9%
Distinct1500
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
2025-03-29T14:48:52.281710image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length19
Median length8
Mean length8.0186953
Min length3

Characters and Unicode

Total characters2013326
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique470 ?
Unique (%)0.2%

Sample

1st row260 g/km
2nd row- (g/km)
3rd row- (g/km)
4th row225 g/km
5th row- (g/km)
ValueCountFrequency (%)
g/km 245650
48.6%
36707
 
7.3%
0 8533
 
1.7%
119 4815
 
1.0%
km 4336
 
0.9%
reichweite 4324
 
0.9%
114 3883
 
0.8%
139 3389
 
0.7%
130 3378
 
0.7%
109 3363
 
0.7%
Other values (1199) 187019
37.0%
2025-03-29T14:48:53.157817image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
254318
12.6%
m 249996
12.4%
k 249987
12.4%
/ 246582
12.2%
g 245650
12.2%
1 215286
10.7%
2 70687
 
3.5%
3 51653
 
2.6%
0 50294
 
2.5%
4 48153
 
2.4%
Other values (38) 330720
16.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2013326
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
254318
12.6%
m 249996
12.4%
k 249987
12.4%
/ 246582
12.2%
g 245650
12.2%
1 215286
10.7%
2 70687
 
3.5%
3 51653
 
2.6%
0 50294
 
2.5%
4 48153
 
2.4%
Other values (38) 330720
16.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2013326
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
254318
12.6%
m 249996
12.4%
k 249987
12.4%
/ 246582
12.2%
g 245650
12.2%
1 215286
10.7%
2 70687
 
3.5%
3 51653
 
2.6%
0 50294
 
2.5%
4 48153
 
2.4%
Other values (38) 330720
16.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2013326
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
254318
12.6%
m 249996
12.4%
k 249987
12.4%
/ 246582
12.2%
g 245650
12.2%
1 215286
10.7%
2 70687
 
3.5%
3 51653
 
2.6%
0 50294
 
2.5%
4 48153
 
2.4%
Other values (38) 330720
16.4%

mileage_in_km
Real number (ℝ)

Distinct71766
Distinct (%)28.6%
Missing152
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean85340.016
Minimum0
Maximum3800000
Zeros202
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2025-03-29T14:48:53.393231image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q124904
median67500
Q3126500
95-th percentile232000
Maximum3800000
Range3800000
Interquartile range (IQR)101596

Descriptive statistics

Standard deviation78717.061
Coefficient of variation (CV)0.92239333
Kurtosis60.312251
Mean85340.016
Median Absolute Deviation (MAD)48342
Skewness3.0518562
Sum2.1414114 × 1010
Variance6.1963757 × 109
MonotonicityNot monotonic
2025-03-29T14:48:53.761369image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 6982
 
2.8%
50 1533
 
0.6%
15 1216
 
0.5%
20 1208
 
0.5%
5 1107
 
0.4%
100 1062
 
0.4%
150000 968
 
0.4%
100000 959
 
0.4%
5000 934
 
0.4%
125000 774
 
0.3%
Other values (71756) 234184
93.3%
ValueCountFrequency (%)
0 202
 
0.1%
1 296
 
0.1%
2 255
 
0.1%
3 108
 
< 0.1%
4 76
 
< 0.1%
5 1107
0.4%
6 185
 
0.1%
7 139
 
0.1%
8 216
 
0.1%
9 200
 
0.1%
ValueCountFrequency (%)
3800000 1
< 0.1%
2830000 1
< 0.1%
2580000 1
< 0.1%
2390000 1
< 0.1%
2300000 1
< 0.1%
2230456 1
< 0.1%
2223400 1
< 0.1%
2190000 1
< 0.1%
2106511 1
< 0.1%
2100000 1
< 0.1%
Distinct200945
Distinct (%)80.0%
Missing1
Missing (%)< 0.1%
Memory size1.9 MiB
2025-03-29T14:48:54.369868image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length50
Median length39
Mean length34.755056
Min length1

Characters and Unicode

Total characters8726230
Distinct characters189
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique182215 ?
Unique (%)72.6%

Sample

1st row2.0 V6 TB
2nd rowQ4 Allrad, 3.2L GTA
3rd rowALFA ROME 916
4th row2.0 16V Twin Spark L
5th row3.0i Super V6, absoluter Topzustand !
ValueCountFrequency (%)
navi 33392
 
2.6%
25379
 
2.0%
2.0 23561
 
1.8%
led 21239
 
1.7%
tdi 20715
 
1.6%
tsi 18786
 
1.5%
dsg 14631
 
1.1%
pdc 13871
 
1.1%
1.0 13330
 
1.0%
klima 12567
 
1.0%
Other values (100424) 1086186
84.6%
2025-03-29T14:48:54.994158image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1042276
 
11.9%
i 382671
 
4.4%
e 376932
 
4.3%
a 347252
 
4.0%
t 300926
 
3.4%
A 265750
 
3.0%
o 265216
 
3.0%
r 263116
 
3.0%
n 247541
 
2.8%
S 245460
 
2.8%
Other values (179) 4989090
57.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8726230
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1042276
 
11.9%
i 382671
 
4.4%
e 376932
 
4.3%
a 347252
 
4.0%
t 300926
 
3.4%
A 265750
 
3.0%
o 265216
 
3.0%
r 263116
 
3.0%
n 247541
 
2.8%
S 245460
 
2.8%
Other values (179) 4989090
57.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8726230
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1042276
 
11.9%
i 382671
 
4.4%
e 376932
 
4.3%
a 347252
 
4.0%
t 300926
 
3.4%
A 265750
 
3.0%
o 265216
 
3.0%
r 263116
 
3.0%
n 247541
 
2.8%
S 245460
 
2.8%
Other values (179) 4989090
57.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8726230
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1042276
 
11.9%
i 382671
 
4.4%
e 376932
 
4.3%
a 347252
 
4.0%
t 300926
 
3.4%
A 265750
 
3.0%
o 265216
 
3.0%
r 263116
 
3.0%
n 247541
 
2.8%
S 245460
 
2.8%
Other values (179) 4989090
57.2%

Interactions

2025-03-29T14:48:32.628165image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-29T14:48:31.703122image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-29T14:48:33.151276image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-29T14:48:32.104815image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Correlations

2025-03-29T14:48:55.055802image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Unnamed: 0brandcolormileage_in_kmtransmission_type
Unnamed: 01.0000.8910.086-0.0990.151
brand0.8911.0000.1120.0230.249
color0.0860.1121.0000.0140.044
mileage_in_km-0.0990.0230.0141.0000.006
transmission_type0.1510.2490.0440.0061.000

Missing values

2025-03-29T14:48:34.112044image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-29T14:48:35.458689image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-29T14:48:37.646744image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0brandmodelcolorregistration_dateyearprice_in_europower_kwpower_pstransmission_typefuel_typefuel_consumption_l_100kmfuel_consumption_g_kmmileage_in_kmoffer_description
00alfa-romeoAlfa Romeo GTVred10/199519951300148201ManualPetrol10,9 l/100 km260 g/km160500.02.0 V6 TB
11alfa-romeoAlfa Romeo 164black02/1995199524900191260ManualPetrolNaN- (g/km)190000.0Q4 Allrad, 3.2L GTA
22alfa-romeoAlfa Romeo Spiderblack02/199519955900110150UnknownPetrolNaN- (g/km)129000.0ALFA ROME 916
33alfa-romeoAlfa Romeo Spiderblack07/199519954900110150ManualPetrol9,5 l/100 km225 g/km189500.02.0 16V Twin Spark L
44alfa-romeoAlfa Romeo 164red11/1996199617950132179ManualPetrol7,2 l/100 km- (g/km)96127.03.0i Super V6, absoluter Topzustand !
55alfa-romeoAlfa Romeo Spiderred04/199619967900110150ManualPetrol9,5 l/100 km225 g/km47307.02.0 16V Twin Spark
66alfa-romeoAlfa Romeo 145red12/199619963500110150ManualPetrol8,8 l/100 km210 g/km230000.0Quadrifoglio
77alfa-romeoAlfa Romeo 164black07/199619965500132179ManualPetrol13,4 l/100 km320 g/km168000.0(3.0) V6 Super
88alfa-romeoAlfa Romeo Spiderblack07/199619968990141192ManualPetrol11 l/100 km265 g/km168600.0|HU:neu|Klimaanlage|Youngtimer|
99alfa-romeoAlfa Romeo Spiderblack01/199619966976110150ManualPetrol9,2 l/100 km220 g/km99000.02.0 T.Spark L *Klima *2.Hand *Zahnriemen
Unnamed: 0brandmodelcolorregistration_dateyearprice_in_europower_kwpower_pstransmission_typefuel_typefuel_consumption_l_100kmfuel_consumption_g_kmmileage_in_kmoffer_description
251069251069volvoVolvo V90 Cross Countrysilver03/2023202377900173235AutomaticDiesel5,5 l/100 km144 g/km2500.0B5 D AWD Geartronic ULTIMATE
251070251070volvoVolvo V90 Cross Countrysilver01/2023202365422145197AutomaticDiesel6,5 l/100 km170 g/km1506.0B4 DIESEL PLUS AWD MY23 SELEKT
251071251071volvoVolvo XC60silver04/2023202381350228310AutomaticHybrid7,4 l/100 km167 g/km60.0XC 60 T8 AWD Ultimate Dark PHEV NAVI,AHK,STHZ,AHK,
251072251072volvoVolvo XC60silver05/2023202355400145197AutomaticDiesel5,6 l/100 km142 g/km5000.0B4 Autom. Plus Dark Keyless-Start/Klima/LED/BC
251073251073volvoVolvo XC60silver03/2023202354500145197AutomaticDiesel5,6 l/100 km142 g/km5900.0B4 Autom. Plus Dark Sitzhzg.
251074251074volvoVolvo XC40white04/2023202357990192261AutomaticHybridNaN43 km Reichweite1229.0Plus Bright T5 Recharge Intellisafe*Surround+Pilot
251075251075volvoVolvo XC90white03/2023202389690173235AutomaticDiesel7,6 l/100 km202 g/km4900.0B5 AWD Diesel Ultimate Dark 7-Sitzer Massage Four-
251076251076volvoVolvo V60white05/2023202361521145197AutomaticDiesel4,7 l/100 km125 g/km1531.0B4 D Plus Dark 145 kW, 5-türig (Diesel)
251077251077volvoVolvo XC40white05/2023202357890132179AutomaticHybridNaN45 km Reichweite1500.0T5 Recharge Plus Dark *Standh*360°*beh.Lenk
251078251078volvoVolvo XC40gold03/2023202352900160218AutomaticElectricNaN438 km Reichweite50.0Ultimate Recharge Twin Motor AHK GJR